


'''simple docstring'''
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class      lowerCamelCase_  (      unittest.TestCase ):






      @property
      def lowercase_				(   self							:       List[str]					):


          '''simple docstring'''


          torch.manual_seed(0					)
          UpperCAmelCase__     :						Tuple						=							UNetaDModel(
              block_out_channels=(32, 64)		,		layers_per_block=2		,		sample_size=32		,		in_channels=3		,		out_channels=3		,		down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')		,		up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')		,		)
          return model






      def lowercase_				(   self							:       List[str]					):


          '''simple docstring'''


          UpperCAmelCase__     :						Any						=							self.dummy_uncond_unet
          UpperCAmelCase__     :						str						=							ScoreSdeVeScheduler()

          UpperCAmelCase__     :						Tuple						=							ScoreSdeVePipeline(unet=_A		,		scheduler=_A					)
          sde_ve.to(_A					)
          sde_ve.set_progress_bar_config(disable=_A					)

          UpperCAmelCase__     :						int						=							torch.manual_seed(0					)
          UpperCAmelCase__     :						Any						=							sde_ve(num_inference_steps=2		,		output_type='''numpy'''		,		generator=_A					).images

          UpperCAmelCase__     :						Tuple						=							torch.manual_seed(0					)
          UpperCAmelCase__     :						Dict						=							sde_ve(num_inference_steps=2		,		output_type='''numpy'''		,		generator=_A		,		return_dict=_A					)[
              0
          ]

          UpperCAmelCase__     :						Optional[Any]						=							image[0, -3:, -3:, -1]
          UpperCAmelCase__     :						List[Any]						=							image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          UpperCAmelCase__     :						Union[str, Any]						=							np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]					)

          assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
          assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2




@slow
@require_torch
class      lowerCamelCase_  (      unittest.TestCase ):






      def lowercase_				(   self							:       Optional[int]					):


          '''simple docstring'''


          UpperCAmelCase__     :						Optional[int]						=							'''google/ncsnpp-church-256'''
          UpperCAmelCase__     :						Dict						=							UNetaDModel.from_pretrained(_A					)

          UpperCAmelCase__     :						Optional[Any]						=							ScoreSdeVeScheduler.from_pretrained(_A					)

          UpperCAmelCase__     :						int						=							ScoreSdeVePipeline(unet=_A		,		scheduler=_A					)
          sde_ve.to(_A					)
          sde_ve.set_progress_bar_config(disable=_A					)

          UpperCAmelCase__     :						Any						=							torch.manual_seed(0					)
          UpperCAmelCase__     :						int						=							sde_ve(num_inference_steps=10		,		output_type='''numpy'''		,		generator=_A					).images

          UpperCAmelCase__     :						Any						=							image[0, -3:, -3:, -1]

          assert image.shape == (1, 256, 256, 3)

          UpperCAmelCase__     :						List[str]						=							np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]					)

          assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2








"""simple docstring"""
def 							A						(				__snake_case: int = 3 ,     __snake_case: int = 7 ,     __snake_case: int = 1_0_0_0_0_0_0  )    ->		int:

       """simple docstring"""





       __magic_name__      				=    0
       __magic_name__      				=    1

       for current_denominator in range(1 ,     limit + 1  ):
              __magic_name__      				=    current_denominator * numerator // denominator
              if current_denominator % denominator == 0:
                     current_numerator -= 1
              if current_numerator * max_denominator > current_denominator * max_numerator:
                     __magic_name__      				=    current_numerator
                     __magic_name__      				=    current_denominator
       return max_numerator


if __name__ == "__main__":
 print(solution(numerator=3, denominator=7, limit=1_0_0_0_0_0_0))